Adaptive Control for a Physics-Informed Model of a Thermal Energy Distribution System: Qualitative Analysis

TL;DR

This paper develops an adaptive control approach for a physics-informed model of a thermal energy system, demonstrating significant error reduction in controlling a glycol heat exchanger with minimal computational cost.

Contribution

It introduces an adaptive control formulation for linear systems in integrated energy systems, specifically applied to a glycol heat exchanger, addressing uncertainties in system dynamics.

Findings

Adaptive control reduced error metrics by 30-75%.

Minimal computational overhead was required for control implementation.

Significant control effort was induced, indicating need for further study.

Abstract

Integrated energy systems (IES) are complex heterogeneous architectures that typically encompass power sources, hydrogen electrolyzers, energy storage, and heat exchangers. This integration is achieved through operating control strategy optimization. However, the lack of physical understanding as to how these systems evolve over time introduces uncertainties that hinder reliable application thereof. Techniques that can accommodate such uncertainties are fundamental for ensuring proper operation of these systems. Unfortunately, no unifying methodology exists for accommodating uncertainties in this regard. That being said, adaptive control (AC) is a discipline that may allow for accommodating such uncertainties in real-time. In the present work, we derive an AC formulation for linear systems in which all states are observable and apply it to the control of a glycol heat exchanger (GHX) in an IES. Based on prior research in which we quantified the uncertainties of the GHXs system dynamics, we introduced an error of 50% on four terms of the nominal model. In the case where a linear quadratic regulator is used as the nominal control for the reference system, we found that employing AC can reduce the mean absolute error and integral time absolute error by a factor of 30%-75%. This reduction is achieved with minimal computing overhead and control infrastructure, thus underscoring the strength of AC. However, the control effort induced is significant, therefore warranting further study in order to estimate its impact on a physical system. To address further challenges, including partially observable and non-linear dynamics, enhancements of the linear formulation are currently being developed.